Under the hard constraints of a real-time vision-based UAV system, an ideal tracker should be efficient to gain more computing capability for sensor fusion, high-level control, etc.
Compared with other discriminative tracking methods, correlation filter (CF)-based trackers assume circulant shifts of the object sample for filter training.
Efficient element-wise operations in the Fourier domain can guarantee real-time performance, which is favorably suitable for UAV tracking applications.
Thus, CF-based tracking methods have been extensively studied in recent years [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. In this process, however, the circulant shifting operation and extended search region introduce synthetic samples and unwarranted noise separately [7]}.
Thus, the learned filters are easily corrupted and can lead to unrecoverable drift when background clutter, similar objects, or other challenging factors occurred.
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